{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Detecting deforestation in the Amazon rainforest using unsupervised K-means clustering on satellite imagery"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Table of Contents <a class=\"anchor\" id=\"0\"></a>\n",
    "* [Introduction](#1) \n",
    "* [Imports](#2)\n",
    "* [Connecting to ArcGIS](#3)\n",
    "* [Accessing & Visualizing the datasets](#4) \n",
    "* [Data Preparation](#5) \n",
    "* [Model Building](#9)\n",
    "    * [Data Preprocessing](#11)  \n",
    "    * [Model Initialization ](#12)\n",
    "    * [Learning Rate Search ](#13)\n",
    "    * [Model Training ](#14) \n",
    "    * [Result Visualization](#16)\n",
    "* [Conclusion](#23)\n",
    "* [Summary of methods used](#24)\n",
    "* [Data resources](#25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction <a class=\"anchor\" id=\"1\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Deforestation around the world has reached a critical level, causing irreversible damage to environmental sustainability that is contributing to climate change around the world. Widespread forest fires, from the Amazon Basin in Brazil, to the west coast of the United States, are raging all year-round. This notebook will allow us to detect deforested areas in the Brazilian Amazon rainforest, using satellite imagery."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports <a class=\"anchor\" id=\"2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from IPython.display import Image\n",
    "from IPython.display import HTML\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from datetime import datetime as dt\n",
    "\n",
    "import arcgis\n",
    "from arcgis.gis import GIS\n",
    "from arcgis.learn import MLModel, prepare_tabulardata\n",
    "from arcgis.raster import Raster\n",
    "\n",
    "from fastai.vision import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Connecting to ArcGIS <a class=\"anchor\" id=\"3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "gis = GIS(\"home\")   \n",
    "gis_enterp = GIS(profile=\"your_enterprise_profile)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing & Visualizing datasets  <a class=\"anchor\" id=\"4\"></a>\n",
    "\n",
    "Here, we use Sentinel-2 imagery, which has a high resolution of 10m and 13 bands. This imagery is accessed from the ArcGIS Enterprise portal, where it is sourced from the AWS collection. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=fd61b9e0c69c4e14bebd50a9a968348c' target='_blank'>\n",
       "                        <img src='' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=fd61b9e0c69c4e14bebd50a9a968348c' target='_blank'><b>Sentinel-2 Views</b>\n",
       "                        </a>\n",
       "                        <br/>Sentinel-2, 10m Multispectral, Multitemporal, 13-band images with visual renderings and indices.  This Imagery Layer is sourced from the Sentinel-2 on AWS collections and is updated daily with new imagery.  This layer is in beta release.\n",
       "<img src='https://geosaurus.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/imagery16.png' style=\"vertical-align:middle;\">Imagery Layer by esri\n",
       "                        <br/>Last Modified: May 28, 2020\n",
       "                        <br/>21 comments, 1,513,352 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"Sentinel-2 Views\" type:Imagery Layer owner:esri>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get image\n",
    "s2 = gis.content.get('fd61b9e0c69c4e14bebd50a9a968348c')\n",
    "sentinel = s2.layers[0]\n",
    "s2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preparation  <a class=\"anchor\" id=\"5\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define Area of Interest in the Amazon\n",
    "The area of interest is defined using the four latitude and longitude values from a certain region of the Amazon rainforest where a considerable area of forest has been deforested, as can be seen from the images above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#  extent in 3857 for amazon rainforest\n",
    "amazon_extent = {\n",
    "    \"xmin\": -6589488.51,\n",
    "    \"ymin\": -325145.08,\n",
    "    \"xmax\": -6586199.09,\n",
    "    \"ymax\": -327024.74,\n",
    "    \"spatialReference\": {\"wkid\": 3857}\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we select all the scenes from the sentinel imagery containing the area of interest for our study."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "   objectid     acquisitiondate                groupname  cloudcover  \\\n",
       "0   2459960 2020-06-21 14:23:37  20200621T142337_21MTS_0      0.0359   \n",
       "1   3343176 2020-07-06 14:23:39  20200706T142339_21MTS_0      0.0037   \n",
       "2   3620096 2020-07-21 14:23:37  20200721T142336_21MTS_0      0.0091   \n",
       "3   3596177 2020-07-26 14:23:40  20200726T142340_21MTS_0      0.0020   \n",
       "4   1584818 2020-07-31 14:23:38  20200731T142337_21MTS_0      0.0000   \n",
       "5    776568 2020-08-10 14:23:38  20200810T142338_21MTS_0      0.0039   \n",
       "6    556656 2020-08-15 14:23:41  20200815T142340_21MTS_0      0.0087   \n",
       "7   1551821 2020-09-04 14:23:39  20200904T142339_21MTS_0      0.0421   \n",
       "8    224997 2020-09-19 14:23:37  20200919T142336_21MTS_0      0.0160   \n",
       "\n",
       "   dayofyear                                              SHAPE  \\\n",
       "0        173  {\"rings\": [[[-6535992.3057, -302418.3759999983...   \n",
       "1        188  {\"rings\": [[[-6535992.3057, -302418.3759999983...   \n",
       "2        203  {\"rings\": [[[-6535992.3057, -302418.3759999983...   \n",
       "3        208  {\"rings\": [[[-6535992.3057, -302418.3759999983...   \n",
       "4        213  {\"rings\": [[[-6535992.3057, -302418.3759999983...   \n",
       "5        223  {\"rings\": [[[-6535992.3057, -302418.3759999983...   \n",
       "6        228  {\"rings\": [[[-6535992.3057, -302418.3759999983...   \n",
       "7        248  {\"rings\": [[[-6535992.3057, -302418.3759999983...   \n",
       "8        263  {\"rings\": [[[-6535992.3057, -302418.3759999983...   \n",
       "\n",
       "      AcquisitionDate  \n",
       "0 2020-06-21 14:23:37  \n",
       "1 2020-07-06 14:23:39  \n",
       "2 2020-07-21 14:23:37  \n",
       "3 2020-07-26 14:23:40  \n",
       "4 2020-07-31 14:23:38  \n",
       "5 2020-08-10 14:23:38  \n",
       "6 2020-08-15 14:23:41  \n",
       "7 2020-09-04 14:23:39  \n",
       "8 2020-09-19 14:23:37  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The respective scene having the above area is selected\n",
    "selected = sentinel.filter_by(where=\"(Category = 1) AND (cloudcover <=0.05)\",\n",
    "                              geometry=arcgis.geometry.filters.intersects(amazon_extent))\n",
    "df = selected.query(out_fields=\"AcquisitionDate, GroupName, CloudCover, DayOfYear\",\n",
    "                   order_by_fields=\"AcquisitionDate\").sdf\n",
    "\n",
    "df['AcquisitionDate'] = pd.to_datetime(df['acquisitiondate'], unit='ms')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The satellite imagery with the least cloud cover is selected and visualized for further processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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\n",
      "text/plain": [
       "<ImageryLayer url:\"https://sentinel.arcgis.com/arcgis/rest/services/Sentinel2/ImageServer\">"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The scene is selected with the least cloud cover and extracted using the amazon extent\n",
    "amazon_scene = sentinel.filter_by('OBJECTID=1584818')\n",
    "amazon_scene.extent = amazon_extent\n",
    "amazon_scene"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above scene, the brown patches are the deforested areas that are to be identified. This selected scene is then published to the portal."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://pythonapi.playground.esri.com/portal/home/item.html?id=e32110f0b32448469a685a77ffb70ce0' target='_blank'>\n",
       "                        <img src='https://pythonapi.playground.esri.com/portal/portalimages/desktopapp.png' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://pythonapi.playground.esri.com/portal/home/item.html?id=e32110f0b32448469a685a77ffb70ce0' target='_blank'><b>amazon_scene331940</b>\n",
       "                        </a>\n",
       "                        <br/>Analysis Image Service generated from GenerateRaster<img src='https://pythonapi.playground.esri.com/portal/home/js/jsapi/esri/css/images/item_type_icons/imagery16.png' style=\"vertical-align:middle;\">Imagery Layer by arcgis_python\n",
       "                        <br/>Last Modified: July 21, 2021\n",
       "                        <br/>0 comments, 0 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"amazon_scene331940\" type:Imagery Layer owner:arcgis_python>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# publish the scene to the portal\n",
    "amazon_scene.save('amazon_scene'+ str(dt.now().microsecond), gis=gis_enterp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The published imagery of the Amazon rainforest is exported back to an image file on disk for further processing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "raster_amazon_13bands = Raster(\"https://pythonapi.playground.esri.com/ra/rest/services/Hosted/amazon_scene_may26/ImageServer\",\n",
    "                               gis=gis_enterp,\n",
    "                               engine=\"image_server\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualizing the image \n",
    "raster_amazon_13bands.export_image(size=[3330,1880])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Building <a class=\"anchor\" id=\"9\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The first part of model building consists of defining the preprocessors, which will be used to scale the bands before feeding them into the model. The band names use the conventional naming method of the imagery name with an id number appended at the end as follows:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sentinel-2 imagery has 13 bands, of which 4 bands, namely the blue, green, red, and near infrared bands, we will use here for modelling. These bands work well for differentiating green forested areas from barren land. The band information, along with the band name and their respective ids, are obtained for selecting the required bands.    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BandName</th>\n",
       "      <th>WavelengthMin</th>\n",
       "      <th>WavelengthMax</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>B1_Aerosols</td>\n",
       "      <td>433</td>\n",
       "      <td>453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B2_Blue</td>\n",
       "      <td>458</td>\n",
       "      <td>522</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>B3_Green</td>\n",
       "      <td>543</td>\n",
       "      <td>577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>B4_Red</td>\n",
       "      <td>650</td>\n",
       "      <td>680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>B5_RedEdge</td>\n",
       "      <td>698</td>\n",
       "      <td>712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>B6_RedEdge</td>\n",
       "      <td>733</td>\n",
       "      <td>747</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>B7_RedEdge</td>\n",
       "      <td>773</td>\n",
       "      <td>793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>B8_NearInfraRed</td>\n",
       "      <td>784</td>\n",
       "      <td>899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>B8A_NarrowNIR</td>\n",
       "      <td>855</td>\n",
       "      <td>875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>B9_WaterVapour</td>\n",
       "      <td>935</td>\n",
       "      <td>955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>B10_Cirrus</td>\n",
       "      <td>1360</td>\n",
       "      <td>1390</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B11_ShortWaveInfraRed</td>\n",
       "      <td>1565</td>\n",
       "      <td>1655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>B12_ShortWaveInfraRed</td>\n",
       "      <td>2100</td>\n",
       "      <td>2280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 BandName WavelengthMin WavelengthMax\n",
       "0             B1_Aerosols           433           453\n",
       "1                 B2_Blue           458           522\n",
       "2                B3_Green           543           577\n",
       "3                  B4_Red           650           680\n",
       "4              B5_RedEdge           698           712\n",
       "5              B6_RedEdge           733           747\n",
       "6              B7_RedEdge           773           793\n",
       "7         B8_NearInfraRed           784           899\n",
       "8           B8A_NarrowNIR           855           875\n",
       "9          B9_WaterVapour           935           955\n",
       "10             B10_Cirrus          1360          1390\n",
       "11  B11_ShortWaveInfraRed          1565          1655\n",
       "12  B12_ShortWaveInfraRed          2100          2280"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the band names and their ids, sentinel images have 13 bands\n",
    "pd.DataFrame(amazon_scene.key_properties()['BandProperties'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Hosted/amazon_scene_may26'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the imagery name to define the band names\n",
    "raster_amazon_13bands.name"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the imagery name is 'Hosted/amazon_scene_april9'. Subsequently, the names of the blue, green, red, and near infrared bands would be 'Hosted/amazon_scene_april9_1', 'Hosted/amazon_scene_april9_2', 'Hosted/amazon_scene_april9_3', 'Hosted/amazon_scene_april9_7' respectively. These bands will be used for defining the preprocessors. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Pre-processing <a class=\"anchor\" id=\"11\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The four bands are listed in the preprocessors for scaling, with the last item as the designated scaler as follows. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "preprocessors = [('Hosted/amazon_scene_may26_1', \n",
    "                  'Hosted/amazon_scene_may26_2', \n",
    "                  'Hosted/amazon_scene_may26_3',\n",
    "                  'Hosted/amazon_scene_may26_7', MinMaxScaler())]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, in the explanatory raster, we pass the name of the explanatory raster and the selected bands by their id's — 1 for blue, 2 for green, 3 for red, and 7 for NIR, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data is prepared for the MLModel using the selected scene and the preprocessors\n",
    "data = prepare_tabulardata(explanatory_rasters=[(raster_amazon_13bands,(1,2,3,7))], preprocessors=preprocessors)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hosted/amazon_scene_may26_1</th>\n",
       "      <th>Hosted/amazon_scene_may26_2</th>\n",
       "      <th>Hosted/amazon_scene_may26_3</th>\n",
       "      <th>Hosted/amazon_scene_may26_7</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>647</th>\n",
       "      <td>820</td>\n",
       "      <td>695</td>\n",
       "      <td>406</td>\n",
       "      <td>2508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5345</th>\n",
       "      <td>1144</td>\n",
       "      <td>1155</td>\n",
       "      <td>1181</td>\n",
       "      <td>2303</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28764</th>\n",
       "      <td>818</td>\n",
       "      <td>707</td>\n",
       "      <td>389</td>\n",
       "      <td>2901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42042</th>\n",
       "      <td>1201</td>\n",
       "      <td>1252</td>\n",
       "      <td>1257</td>\n",
       "      <td>2596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61515</th>\n",
       "      <td>802</td>\n",
       "      <td>700</td>\n",
       "      <td>388</td>\n",
       "      <td>2410</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Hosted/amazon_scene_may26_1  Hosted/amazon_scene_may26_2  \\\n",
       "647                            820                          695   \n",
       "5345                          1144                         1155   \n",
       "28764                          818                          707   \n",
       "42042                         1201                         1252   \n",
       "61515                          802                          700   \n",
       "\n",
       "       Hosted/amazon_scene_may26_3  Hosted/amazon_scene_may26_7  \n",
       "647                            406                         2508  \n",
       "5345                          1181                         2303  \n",
       "28764                          389                         2901  \n",
       "42042                         1257                         2596  \n",
       "61515                          388                         2410  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# visualization of the data to be processed by the model\n",
    "data.show_batch()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Initialization <a class=\"anchor\" id=\"12\"></a>\n",
    "\n",
    "Once the data is prepared, an unsupervised model of k-means clustering from scikit-learn is used for clustering the pixels into deforested areas and forested areas. The clustering model is passed inside MLModel as follows, with the number of clusters set as three in the parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from arcgis.learn import MLModel, prepare_tabulardata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MLModel(data, 'sklearn.cluster.KMeans', n_clusters=3, init='k-means++', random_state=43)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Training <a class=\"anchor\" id=\"14\"></a>\n",
    "Now the model is ready to be trained, and will label the pixels as being one of three classes, either forested, slightly deforested, or highly deforested.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# here model is trained which would label the pixels into the designated classes \n",
    "model.fit()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Hosted/amazon_scene_may26_1</th>\n",
       "      <th>Hosted/amazon_scene_may26_2</th>\n",
       "      <th>Hosted/amazon_scene_may26_3</th>\n",
       "      <th>Hosted/amazon_scene_may26_7</th>\n",
       "      <th>prediction_results</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>25429</th>\n",
       "      <td>811</td>\n",
       "      <td>709</td>\n",
       "      <td>391</td>\n",
       "      <td>3294</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30113</th>\n",
       "      <td>829</td>\n",
       "      <td>730</td>\n",
       "      <td>397</td>\n",
       "      <td>3261</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31345</th>\n",
       "      <td>835</td>\n",
       "      <td>767</td>\n",
       "      <td>419</td>\n",
       "      <td>2783</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32640</th>\n",
       "      <td>986</td>\n",
       "      <td>1015</td>\n",
       "      <td>780</td>\n",
       "      <td>3028</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55783</th>\n",
       "      <td>815</td>\n",
       "      <td>708</td>\n",
       "      <td>421</td>\n",
       "      <td>2958</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Hosted/amazon_scene_may26_1  Hosted/amazon_scene_may26_2  \\\n",
       "25429                          811                          709   \n",
       "30113                          829                          730   \n",
       "31345                          835                          767   \n",
       "32640                          986                         1015   \n",
       "55783                          815                          708   \n",
       "\n",
       "       Hosted/amazon_scene_may26_3  Hosted/amazon_scene_may26_7  \\\n",
       "25429                          391                         3294   \n",
       "30113                          397                         3261   \n",
       "31345                          419                         2783   \n",
       "32640                          780                         3028   \n",
       "55783                          421                         2958   \n",
       "\n",
       "       prediction_results  \n",
       "25429                   2  \n",
       "30113                   2  \n",
       "31345                   2  \n",
       "32640                   2  \n",
       "55783                   2  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the labelled pixels can be visualized as follows with the last column returning the predicted labels by the model \n",
    "model.show_results()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Deforestation Clusters Prediction<a class=\"anchor\" id=\"21\"></a>\n",
    "\n",
    "Next, the trained model is used to predict clusters within the entire selected scene of the Amazon rainforest. This is passed as the explanatory raster, with the prediction type as raster and a local path provided for output. The `output_layer_name` parameter can also be used for publishing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Published Item ID is :  3df30cd12a48421dade893490c967c62\n"
     ]
    }
   ],
   "source": [
    "pred_new = model.predict(explanatory_rasters=[raster_amazon_13bands],\n",
    "                         prediction_type='raster',\n",
    "                         output_layer_name=('deforest_predicted2'+str(dt.now().microsecond)),\n",
    "                         output_raster_path=r\"/tmp/result5.tif\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result Visualization<a class=\"anchor\" id=\"16\"></a>\n",
    "\n",
    "The resulting raster with the predicted classes of deforested areas and forested areas is now read back for visualization. The predicted local raster can be accessed here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"item_container\" style=\"height: auto; overflow: hidden; border: 1px solid #cfcfcf; border-radius: 2px; background: #f6fafa; line-height: 1.21429em; padding: 10px;\">\n",
       "                    <div class=\"item_left\" style=\"width: 210px; float: left;\">\n",
       "                       <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=b81b89aac4cd4e08bcd7cd400fac558f' target='_blank'>\n",
       "                        <img src='' width='200' height='133' class=\"itemThumbnail\">\n",
       "                       </a>\n",
       "                    </div>\n",
       "\n",
       "                    <div class=\"item_right\"     style=\"float: none; width: auto; overflow: hidden;\">\n",
       "                        <a href='https://geosaurus.maps.arcgis.com/home/item.html?id=b81b89aac4cd4e08bcd7cd400fac558f' target='_blank'><b>amazon_deforest_result</b>\n",
       "                        </a>\n",
       "                        <br/><img src='https://geosaurus.maps.arcgis.com/home/js/jsapi/esri/css/images/item_type_icons/layers16.png' style=\"vertical-align:middle;\">Image Collection by arcgis_python\n",
       "                        <br/>Last Modified: April 16, 2021\n",
       "                        <br/>0 comments, 12 views\n",
       "                    </div>\n",
       "                </div>\n",
       "                "
      ],
      "text/plain": [
       "<Item title:\"amazon_deforest_result\" type:Image Collection owner:arcgis_python>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "amazon_predict = gis.content.get('b81b89aac4cd4e08bcd7cd400fac558f')\n",
    "amazon_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os, zipfile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "filepath_new = amazon_predict.download(file_name=amazon_predict.name)\n",
    "with zipfile.ZipFile(filepath_new, 'r') as zip_ref:\n",
    "    zip_ref.extractall(Path(filepath_new).parent)\n",
    "output_path = Path(os.path.join(os.path.splitext(filepath_new)[0]))\n",
    "output_path = os.path.join(output_path, \"result5.tif\")    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "raster_predict = Raster(output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "raster_predict.export_image(size=[3290,1880])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model has correctly labelled the deforested areas in white, distinguishing them from the rest of the forested areas in black.\n",
    "\n",
    "The boundaries of the detected deforested areas could be further extracted into polygons using the convert raster to feature function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion<a class=\"anchor\" id=\"23\"></a>\n",
    "\n",
    "In this sample notebook we were able to detect deforestation in the Amazon rainforest using the unsupervised model of k-means clustering on satellite imagery. This was implemented via the MLModel framework, which exhibited the application of several unsupervised models from the scikit learn library on raster imagery."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary of methods used <a class=\"anchor\" id=\"24\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Method | Description | Examples |\n",
    "| -| - |-|\n",
    "| prepare_tabulardata| prepare data including imputation, normalization and train-test split  |prepare data ready for fitting a  MLModel \n",
    "| MLModel() | select the ML algorithm to be used for fitting  | any supervised and unsupervised models from scikit learn can be used\n",
    "| model.fit() | train a model   | training the unsupervised model with suitable input \n",
    "| model.score() | find the appropriate model metric of the trained model  | returns suitable value after training the unsupervised MLModel\n",
    "| model.predict() | predict on a test set | predict values using the trained models on the trained data itself"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data resources <a class=\"anchor\" id=\"25\"></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Dataset | Source | Link |\n",
    "| -| - |-|\n",
    "| sat imagery| sentinel2  |https://registry.opendata.aws/sentinel-2/|"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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    "name": "ipython",
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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